Sign Language Recognition (SLR) system is a method which allow deaf people to communicate with society. In this study, Real-Time Sign Language recognition system was proposed by using the surface Electromyography (sEMG). To this purpose, sEMG data acquired from subject right forearm for all twenty six American Sign Language gestures. Raw sEMG data was filtered, feature extracted and fed into classification. Support Vector Machine (SVM) with one vs. all approach was used for multi class classification. The experiment result of offline system is reaching a recognition rate of 91.% accuracy and real-time system has a recognition rate of 82.3% accuracy. The results of the proposed system shows that sEMG signal can be used for Real-Time SLR systems.

Real-Time American Sign Language Recognition System Using Surface EMG Signal

Sign language maps letters, words, and expressions of a certain language to a set of hand gestures enabling an individual to communicate by using hands and gestures rather than by speaking. Systems capable of recognizing sign-language symbols can be used for communication with the hearing-impaired. This paper represents the first attempt to recognize two-handed signs from the Unified Arabic Sign Language Dictionary using the CyberGlove and support vector machines (SVMs). 20 samples from each of 100 two-handed signs were collected from two adult signers. Because the signs are of different lengths, time division is used to standardize sign length. The duration of every sign is divided into a specific number of segments, and the mean and standard deviation of each segment are used to represent the signal in the segment. After pre-processing, principal component analysis is used for feature extraction. For recognition, a SVM is trained on 15 samples from each sign. The performance is obtained by testing the trained SVM on the remaining five samples from each sign. A recognition rate of 99.6% on the testing data is obtained.

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Recognition of Two-Handed Arabic Signs Using the CyberGlove

This article focuses on the use of data gloves for human-computer interaction concepts, where external sensors cannot always fully observe the user’s hand. A good concept hereby allows to intuitively switch the interaction context on demand by using different hand gestures. The recognition of various, possibly complex hand gestures, however, introduces unintentional overhead to the system. Consequently, we present a data glove prototype comprising a glove-embedded gesture classifier utilizing data from Inertial Measurement Units (IMUs) in the fingertips. In an extensive set of experiments with 57 participants, our system was tested with 22 hand gestures, all taken from the French Sign Language (LSF) alphabet. Results show that our system is capable of detecting the LSF alphabet with a mean accuracy score of 92% and an F1 score of 91%, using complementary filter with a gyroscope-to-accelerometer ratio of 93%. Our approach has also been compared to the local fusion algorithm on an IMU motion sensor, showing faster settling times and less delays after gesture changes. Real-time performance of the recognition is shown to occur within 63 milliseconds, allowing fluent use of the gestures via Bluetooth-connected systems.

Real-Time and Embedded Detection of Hand Gestures with an IMU-Based Glove

Sign Language Recognition (SLR) system is a novel method that allows hard of hearing people to communicate with society. In this study, an American Sign Language (ASL) recognition system was proposed by using the surface Electromyography (sEMG). The objective of this study is to recognize the American Sign Language alphabet letters and allow users to spell words and sentences. For this purpose, sEMG signals are acquired from subject's right forearm for 27 American Sign Language gestures, 26 English alphabet letters, and one for home position. Time domain, frequency domain (band power), power spectral density (band power), and average power features were used as the feature extraction methods. After feature extraction, Principal Component Analysis (PCA) was applied to obtain uncorrelated features. As a classification method, Support Vector Machine and Ensemble Learning algorithm were used and their performances were compared with tabulated results. In conclusion, the results of this study show that sEMG signal can be used for SLR systems.

/pdf/american-sign-language-recognition-system-by-using-surface-1vpyfv3pxk.pdf

American Sign Language Recognition system by using surface EMG signal

Sign language is a communication tool for deaf and dumb people that includes known signs or body gestures to transfer meanings It uses shapes, directions, movements of hands, and also facial expressions A sign not only transmits a word but also conveys a tone Many of the deaf people are not only able to speak, but also not able to write or read a language, so developing sign language translation or in other words sign language recognition (SLR) system can be very vital in their life The SLR is extremely desired because of its capability to overcome the obstacles between deaf and normal people It is one of the most important research fields in the human computer interaction studies Hence, this paper presents an overview of the recent main research works with the vision-based SLR system, and the existing recognition techniques are discussed Next, we focus on video-based SLR system and perform continuous SLR within video sequences

Vision-Based Sign Language Classification: A Directional Review

Human beings usually interact with each other either by via a natural language channel such as speech and writing, or by body languag, e.g. hand gestures, head gestures, facial expression, lip motion and so on. As a part of natural language understanding, sign language recognition is very important. On one hand, it is one of the main methods of human-computer interaction in virtual reality; on the other hand, it is an auxiliary tool for a deaf-mute to communicate with ordinary people through computer. In this paper the process of building a simple word-level sign language recognition system is presented, and the method for recognizing sign language words is also proposed.

Liu wei

Papers

A simple sign language recognition system based on data glove